Queen's University Belfast - NISRA BDR Programme (Propelling Growth in Northern Ireland: Measuring and Explaining Business Productivity)

我们的研究将有助于这一现有的文献提供了更详细的图片北方爱尔兰生产力。我们确定存在哪些政策机会，以提高各公司、各部门和各地区的生产率和效率。我们将通过利用NIABI，BESES和BRES的公司层面数据来实现这一目标，使我们能够解决有关NI生产力问题的一些长期存在的问题，由于数据的有限性，这些问题迄今为止仍未得到解答（例如Mac Flynn，2016; Goldrick-Kelly和Mac Flynn，2018）。我们的研究将在三个主要方面做出贡献。首先，除了计算标准的生产率指标外，我们还将使用公司层面的数据来构建和估计NI经济中多个部门的大量公司的生产函数。这将构成对NI经济中公司数据集的最大分析：相比之下，德里菲尔德和拉沃拉托里（2020）的研究仅限于来自制造业的300家公司，来源于FAME数据库。第二，我们的方法将使我们能够计算全要素生产率（TFP）的措施和帕累托-库普曼斯和德布鲁-法雷尔的措施的技术效率（见奥利和帕克斯，1996年;莱文索恩和彼得林，2003年;费尔和普里蒙特，1995年;西玛和威尔逊，2002年）。这将为当地经济中劳动力和资本之间的相互作用提供新的见解。利用生产率指标的时间和横截面变化，我们就可以了解在哪些方面最需要采取干预措施来提高北爱尔兰的生产率。第三，我们的建议将使我们能够评估哪些政策--过去、现在和未来--在刺激就业、投资、创新和最终生产率方面最成功。通过利用跨经济部门、地方政府区域的横截面变化，我们将能够评估哪些公司表现得更好、更有效。这将使政府能够更有针对性地支持提高地区生产力。同样，我们的方法与企业层面的数据，使我们能够确定最佳做法的公司和其他地区和企业家学习的领域。

Our research will contribute to this existing literature by providing a more detailed picture of Northern Irish productivity. We identifying where opportunities exist for policy to raise productivity and efficiency across firms, sectors, and across geographies. We will do this by utilizing the firm level data from the NIABI, BESES and BRES, allowing us to address some of the long standing questions about NI's productivity problem, which so far have remained unanswered due to the limited availability of data (e.g. Mac Flynn, 2016; Goldrick-Kelly and Mac Flynn, 2018). Our research will contribute in three main ways. First, in addition to calculating standard productivity metrics, we will use firm level data to construct and estimate production functions for a large number of firms across multiple sectors within NI's economy. This will constitute the largest analysis of a dataset of firms in NI's economy: by comparison, Driffield and Lavoratori's (2020) study was limited to under 300 firms from only the manufacturing sector, sourced from the FAME database. Second, our approach will allow us to calculate measures of total factor productivity (TFP) and Pareto-Koopmans and Debreu-Farrell measures of technical efficiency (see Olley and Pakes, 1996; Levinsohn and Petrin, 2003; Färe and Primont, 1995; Simar and Wilson, 2002). This will provide a new insight into the interactions between labour and capital across the local economy. Exploiting the temporal and cross-sectional variation in productivity metrics will then allow us to understand where interventions are most needed to raise NI's productivity.Third, our proposal will allow us to assess which policies -past, present and future - are most successful in stimulating employment, investment, innovation, and ultimately productivity. By exploiting cross-sectional variation across economic sectors, local government districts, we will be able to assess where firms are performing better and more efficiently. This will allow for more targeted government support in boosting regional productivity. Similarly, our approach with firm-level data allows us to identify best practices among firms and areas for other regions and entrepreneurs to learn from.